Overview

Dataset statistics

Number of variables36
Number of observations839
Missing cells505
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory230.5 KiB
Average record size in memory281.3 B

Variable types

Numeric17
Categorical18
DateTime1

Alerts

Has_Comments has constant value "1" Constant
headline has a high cardinality: 838 distinct values High cardinality
abstract has a high cardinality: 837 distinct values High cardinality
keywords has a high cardinality: 781 distinct values High cardinality
uniqueID has a high cardinality: 839 distinct values High cardinality
lead_paragraph has a high cardinality: 785 distinct values High cardinality
headline.main has a high cardinality: 838 distinct values High cardinality
df_index is highly correlated with monthHigh correlation
month is highly correlated with df_indexHigh correlation
TEXT_LeadParagraph_POS_PNOUN is highly correlated with TEXT_LeadParagraph_ENT_ORG and 1 other fieldsHigh correlation
TEXT_Keywords_POS_PNOUN is highly correlated with TEXT_Keywords_ENT_ORG and 1 other fieldsHigh correlation
TEXT_LeadParagraph_ENT_ORG is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_LeadParagraph_ENT_PERSON is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keyrwords_ENT_PERSON is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
df_index is highly correlated with monthHigh correlation
month is highly correlated with df_indexHigh correlation
TEXT_LeadParagraph_POS_PNOUN is highly correlated with TEXT_LeadParagraph_ENT_ORG and 2 other fieldsHigh correlation
TEXT_Keywords_POS_PNOUN is highly correlated with TEXT_Keywords_ENT_ORG and 1 other fieldsHigh correlation
TEXT_LeadParagraph_ENT_ORG is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_LeadParagraph_ENT_GPE is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_LeadParagraph_ENT_PERSON is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keyrwords_ENT_PERSON is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
df_index is highly correlated with monthHigh correlation
month is highly correlated with df_indexHigh correlation
TEXT_LeadParagraph_POS_PNOUN is highly correlated with TEXT_LeadParagraph_ENT_PERSONHigh correlation
TEXT_Keywords_POS_PNOUN is highly correlated with TEXT_Keywords_ENT_ORG and 1 other fieldsHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_LeadParagraph_ENT_PERSON is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keyrwords_ENT_PERSON is highly correlated with TEXT_Keywords_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_LOC is highly correlated with Has_CommentsHigh correlation
newsdesk is highly correlated with section and 3 other fieldsHigh correlation
TEXT_LeadParagraph_ENT_LOC is highly correlated with Has_CommentsHigh correlation
TEXT_LeadParagraph_ENT_FAC is highly correlated with Has_CommentsHigh correlation
section is highly correlated with newsdesk and 3 other fieldsHigh correlation
TEXT_Keywords_ENT_NORP is highly correlated with Has_CommentsHigh correlation
comment_size is highly correlated with Has_CommentsHigh correlation
TEXT_headline.main_POS_NOUN is highly correlated with Has_CommentsHigh correlation
TEXT_Keywords_ENT_FAC is highly correlated with Has_CommentsHigh correlation
Has_Comments is highly correlated with TEXT_Keywords_ENT_LOC and 10 other fieldsHigh correlation
material is highly correlated with newsdesk and 2 other fieldsHigh correlation
print_section is highly correlated with newsdesk and 2 other fieldsHigh correlation
df_index is highly correlated with monthHigh correlation
newsdesk is highly correlated with section and 9 other fieldsHigh correlation
section is highly correlated with newsdesk and 8 other fieldsHigh correlation
material is highly correlated with newsdesk and 3 other fieldsHigh correlation
word_count is highly correlated with TEXT_Keywords_POS_PNOUN and 2 other fieldsHigh correlation
n_comments is highly correlated with comment_sizeHigh correlation
print_section is highly correlated with newsdesk and 3 other fieldsHigh correlation
print_page is highly correlated with newsdesk and 2 other fieldsHigh correlation
month is highly correlated with df_indexHigh correlation
comment_size is highly correlated with newsdesk and 3 other fieldsHigh correlation
TEXT_LeadParagraph_POS_NOUN is highly correlated with newsdesk and 3 other fieldsHigh correlation
TEXT_LeadParagraph_POS_PNOUN is highly correlated with newsdesk and 6 other fieldsHigh correlation
TEXT_Keywords_POS_NOUN is highly correlated with section and 2 other fieldsHigh correlation
TEXT_Keywords_POS_PNOUN is highly correlated with newsdesk and 5 other fieldsHigh correlation
TEXT_LeadParagraph_ENT_ORG is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_ORG is highly correlated with word_count and 3 other fieldsHigh correlation
TEXT_LeadParagraph_ENT_NORP is highly correlated with TEXT_LeadParagraph_POS_NOUN and 1 other fieldsHigh correlation
TEXT_Keywords_ENT_FAC is highly correlated with TEXT_Keywords_ENT_GPEHigh correlation
TEXT_LeadParagraph_ENT_GPE is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keywords_ENT_GPE is highly correlated with newsdesk and 3 other fieldsHigh correlation
TEXT_Keywords_ENT_LOC is highly correlated with newsdeskHigh correlation
TEXT_LeadParagraph_ENT_PERSON is highly correlated with TEXT_LeadParagraph_POS_PNOUNHigh correlation
TEXT_Keyrwords_ENT_PERSON is highly correlated with word_count and 2 other fieldsHigh correlation
print_section has 252 (30.0%) missing values Missing
print_page has 252 (30.0%) missing values Missing
headline is uniformly distributed Uniform
abstract is uniformly distributed Uniform
uniqueID is uniformly distributed Uniform
lead_paragraph is uniformly distributed Uniform
headline.main is uniformly distributed Uniform
df_index has unique values Unique
uniqueID has unique values Unique
word_count has 19 (2.3%) zeros Zeros
TEXT_LeadParagraph_POS_NOUN has 27 (3.2%) zeros Zeros
TEXT_LeadParagraph_POS_PNOUN has 130 (15.5%) zeros Zeros
TEXT_Keywords_POS_NOUN has 116 (13.8%) zeros Zeros
TEXT_Keywords_POS_PNOUN has 37 (4.4%) zeros Zeros
TEXT_headline.main_POS_PNOUN has 56 (6.7%) zeros Zeros
TEXT_LeadParagraph_ENT_ORG has 555 (66.2%) zeros Zeros
TEXT_Keywords_ENT_ORG has 262 (31.2%) zeros Zeros
TEXT_LeadParagraph_ENT_NORP has 720 (85.8%) zeros Zeros
TEXT_LeadParagraph_ENT_GPE has 450 (53.6%) zeros Zeros
TEXT_Keywords_ENT_GPE has 406 (48.4%) zeros Zeros
TEXT_LeadParagraph_ENT_PERSON has 461 (54.9%) zeros Zeros
TEXT_Keyrwords_ENT_PERSON has 262 (31.2%) zeros Zeros

Reproduction

Analysis started2022-01-11 22:26:53.386349
Analysis finished2022-01-11 22:28:25.120151
Duration1 minute and 31.73 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct839
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8483.678188
Minimum7
Maximum16771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:25.309170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile862.7
Q14367.5
median8275
Q312657
95-th percentile15936
Maximum16771
Range16764
Interquartile range (IQR)8289.5

Descriptive statistics

Standard deviation4818.381176
Coefficient of variation (CV)0.567958976
Kurtosis-1.205589822
Mean8483.678188
Median Absolute Deviation (MAD)4139
Skewness-0.02563644036
Sum7117806
Variance23216797.16
MonotonicityNot monotonic
2022-01-11T16:28:25.586554image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137151
 
0.1%
101781
 
0.1%
112571
 
0.1%
4241
 
0.1%
271
 
0.1%
139001
 
0.1%
82741
 
0.1%
136981
 
0.1%
56561
 
0.1%
146381
 
0.1%
Other values (829)829
98.8%
ValueCountFrequency (%)
71
0.1%
271
0.1%
321
0.1%
481
0.1%
551
0.1%
1101
0.1%
1181
0.1%
1221
0.1%
1801
0.1%
1861
0.1%
ValueCountFrequency (%)
167711
0.1%
167491
0.1%
167471
0.1%
167301
0.1%
166951
0.1%
166461
0.1%
166451
0.1%
166401
0.1%
166241
0.1%
166121
0.1%

newsdesk
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
OpEd
98 
Business
64 
Foreign
54 
Culture
 
48
Metro
 
47
Other values (37)
528 

Length

Max length15
Median length7
Mean length7.059594756
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.5%

Sample

1st rowSports
2nd rowWell
3rd rowUpshot
4th rowForeign
5th rowOpEd

Common Values

ValueCountFrequency (%)
OpEd98
 
11.7%
Business64
 
7.6%
Foreign54
 
6.4%
Culture48
 
5.7%
Metro47
 
5.6%
National41
 
4.9%
Washington39
 
4.6%
Learning32
 
3.8%
Dining31
 
3.7%
RealEstate31
 
3.7%
Other values (32)354
42.2%

Length

2022-01-11T16:28:25.815763image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oped98
 
11.6%
business64
 
7.6%
foreign54
 
6.4%
culture48
 
5.7%
metro47
 
5.6%
national41
 
4.9%
washington39
 
4.6%
learning32
 
3.8%
dining31
 
3.7%
realestate31
 
3.7%
Other values (33)359
42.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

section
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
U.S.
116 
Opinion
115 
New York
63 
World
60 
Business Day
49 
Other values (26)
436 

Length

Max length20
Median length7
Mean length7.483909416
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSports
2nd rowWell
3rd rowThe Upshot
4th rowWorld
5th rowOpinion

Common Values

ValueCountFrequency (%)
U.S.116
13.8%
Opinion115
13.7%
New York63
 
7.5%
World60
 
7.2%
Business Day49
 
5.8%
Arts48
 
5.7%
The Learning Network34
 
4.1%
Food32
 
3.8%
Real Estate31
 
3.7%
Well30
 
3.6%
Other values (21)261
31.1%

Length

2022-01-11T16:28:25.998105image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
u.s116
 
10.3%
opinion115
 
10.2%
new63
 
5.6%
york63
 
5.6%
world60
 
5.3%
business49
 
4.3%
day49
 
4.3%
the49
 
4.3%
arts48
 
4.2%
network34
 
3.0%
Other values (31)484
42.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

material
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
News
653 
Op-Ed
102 
Review
 
28
Interactive Feature
 
19
briefing
 
13
Other values (3)
 
24

Length

Max length19
Median length4
Mean length4.816448153
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNews
2nd rowNews
3rd rowNews
4th rowNews
5th rowOp-Ed

Common Values

ValueCountFrequency (%)
News653
77.8%
Op-Ed102
 
12.2%
Review28
 
3.3%
Interactive Feature19
 
2.3%
briefing13
 
1.5%
Editorial11
 
1.3%
Obituary (Obit)9
 
1.1%
News Analysis4
 
0.5%

Length

2022-01-11T16:28:26.206117image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:26.337128image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
news657
75.4%
op-ed102
 
11.7%
review28
 
3.2%
interactive19
 
2.2%
feature19
 
2.2%
briefing13
 
1.5%
editorial11
 
1.3%
obituary9
 
1.0%
obit9
 
1.0%
analysis4
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

headline
Categorical

HIGH CARDINALITY
UNIFORM

Distinct838
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Homes for Sale in New York and Connecticut
 
2
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance
 
1
Bill Gates Is the Most Interesting Man in the World
 
1
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call
 
1
Already Had Plenty of Trump 2020?
 
1
Other values (833)
833 

Length

Max length112
Median length57
Mean length54.15852205
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique837 ?
Unique (%)99.8%

Sample

1st rowMookie Betts Leads Dodgers’ Stars With a Masterly Performance
2nd rowTaking Baths May Be Good for Your Heart
3rd rowYoung Men Embrace Gender Equality, but They Still Don’t Vacuum
4th row2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears Goal
5th rowIs the Stock Market Rooting for Trump or Biden?

Common Values

ValueCountFrequency (%)
Homes for Sale in New York and Connecticut2
 
0.2%
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance1
 
0.1%
Bill Gates Is the Most Interesting Man in the World1
 
0.1%
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call1
 
0.1%
Already Had Plenty of Trump 2020?1
 
0.1%
How the Virus Slowed the Booming Wind Energy Business1
 
0.1%
Coronavirus Cases Rise Sharply in Prisons Even as They Plateau Nationwide1
 
0.1%
Museums Are Back, but Different: A Visitor’s Guide1
 
0.1%
Jobless Numbers Are ‘Eye-Watering’ but Understate the Crisis1
 
0.1%
For Veterans Day, Some Former Military Officers Reflect on Lessons From Their Parents1
 
0.1%
Other values (828)828
98.7%

Length

2022-01-11T16:28:26.566144image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the312
 
4.1%
a209
 
2.7%
to163
 
2.1%
in145
 
1.9%
of143
 
1.9%
and117
 
1.5%
for88
 
1.2%
is84
 
1.1%
on64
 
0.8%
coronavirus56
 
0.7%
Other values (3094)6225
81.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

abstract
Categorical

HIGH CARDINALITY
UNIFORM

Distinct837
Distinct (%)99.9%
Missing1
Missing (%)0.1%
Memory size6.7 KiB
Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.
 
2
The Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.
 
1
He’s everywhere, this lavender-sweatered Mister Rogers for the curious and quarantined.
 
1
The owner of Neir’s Tavern in Queens said it would close on Sunday, barring a “miracle.” The city, it turns out, works in mysterious ways.
 
1
He’s a bad show, but it’s not low-flow.
 
1
Other values (832)
832 

Length

Max length344
Median length130
Mean length126.8078759
Min length23

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique836 ?
Unique (%)99.8%

Sample

1st rowThe Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.
2nd rowDaily baths reduced the risk of heart disease and stroke.
3rd rowNew studies show traditional views persist about who does what at home, and it’s holding women back.
4th rowThe Chinese city where the outbreak began is seeking to test all its 11 million residents, and the pandemic has forced the fashion industry to take a hard look in the mirror.
5th rowNeither. Wall Street is not as partisan as you think.

Common Values

ValueCountFrequency (%)
Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.2
 
0.2%
The Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.1
 
0.1%
He’s everywhere, this lavender-sweatered Mister Rogers for the curious and quarantined.1
 
0.1%
The owner of Neir’s Tavern in Queens said it would close on Sunday, barring a “miracle.” The city, it turns out, works in mysterious ways.1
 
0.1%
He’s a bad show, but it’s not low-flow.1
 
0.1%
Renewable energy developers have struggled to finish projects as the pandemic disrupts construction and global supply chains.1
 
0.1%
Prison officials have been reluctant to do widespread virus testing even as infection rates are escalating.1
 
0.1%
The visitors may be masked, but the art is gradually coming into full view.1
 
0.1%
With 4.4 million added last week, the five-week total passed 26 million. The struggle by states to field claims has hampered economic recovery.1
 
0.1%
The values that shaped them include leadership, optimism and charting your own course.1
 
0.1%
Other values (827)827
98.6%

Length

2022-01-11T16:28:26.815162image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1065
 
6.1%
a537
 
3.1%
to471
 
2.7%
and459
 
2.6%
of454
 
2.6%
in396
 
2.3%
is184
 
1.1%
for164
 
0.9%
are141
 
0.8%
with135
 
0.8%
Other values (5169)13496
77.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

keywords
Categorical

HIGH CARDINALITY

Distinct781
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
[]
 
33
['Crossword Puzzles']
 
13
['Coronavirus (2019-nCoV)']
 
10
['New York City']
 
4
['Trump, Donald J', 'Presidential Election of 2020', 'United States Politics and Government']
 
2
Other values (776)
777 

Length

Max length1381
Median length159
Mean length170.0059595
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique775 ?
Unique (%)92.4%

Sample

1st row['Baseball', 'World Series', 'Boston Red Sox', 'Los Angeles Dodgers', 'Tampa Bay Rays', 'Bellinger, Cody (1995- )', 'Betts, Mookie (1992- )', 'Kershaw, Clayton']
2nd row['Bathing and Showering', 'Heart', 'Blood Pressure']
3rd row['Work-Life Balance', 'Women and Girls', 'Research', 'Men and Boys', 'Parenting', 'Labor and Jobs', 'United States']
4th row['Coronavirus (2019-nCoV)']
5th row['Presidential Election of 2020', 'United States Economy', 'Stocks and Bonds']

Common Values

ValueCountFrequency (%)
[]33
 
3.9%
['Crossword Puzzles']13
 
1.5%
['Coronavirus (2019-nCoV)']10
 
1.2%
['New York City']4
 
0.5%
['Trump, Donald J', 'Presidential Election of 2020', 'United States Politics and Government']2
 
0.2%
['Television', 'Billions (TV Program)']2
 
0.2%
['Bicycles and Bicycling', 'Women and Girls', 'Coronavirus (2019-nCoV)', 'Traffic Accidents and Safety', 'Commuting', 'Citi Bike', 'New York University', 'Transportation Alternatives', 'New York City']1
 
0.1%
['Museums', 'Coronavirus Reopenings', 'AMERICAN MUSEUM OF NATURAL HISTORY', 'Dallas Museum of Art', 'Gardner, Isabella Stewart, Museum', 'Los Angeles County Museum of Art', 'Metropolitan Museum of Art', 'Museum of Fine Arts (Boston)', 'Museum of Modern Art', 'National Gallery of Art', 'Whitney Museum of American Art', 'Perez, Jorge M, Art Museum of Miami-Dade County', 'Museum of Fine Arts (Houston)', 'Cleveland Museum of Art', 'Sirmans, Franklin', 'Tinterow, Gary', 'Weinberg, Adam D', 'Zumthor, Peter']1
 
0.1%
['Coronavirus Aid, Relief, and Economic Security Act (2020)', 'Coronavirus (2019-nCoV)', 'Unemployment', 'Unemployment Insurance', 'Layoffs and Job Reductions', 'Labor and Jobs', 'United States Economy', 'States (US)']1
 
0.1%
['United States Defense and Military Forces', 'Children and Childhood', 'Careers and Professions', 'Parenting', 'Families and Family Life', 'Defense Department', 'United States Army', 'United States Marine Corps', 'United States Navy']1
 
0.1%
Other values (771)771
91.9%

Length

2022-01-11T16:28:27.054179image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and1310
 
7.9%
coronavirus352
 
2.1%
2019-ncov289
 
1.8%
states277
 
1.7%
united267
 
1.6%
of228
 
1.4%
government190
 
1.2%
politics190
 
1.2%
j150
 
0.9%
150
 
0.9%
Other values (3593)13090
79.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

word_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct642
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1343.942789
Minimum0
Maximum11020
Zeros19
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:27.272198image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile268.7
Q1917.5
median1203
Q31497.5
95-th percentile2539.2
Maximum11020
Range11020
Interquartile range (IQR)580

Descriptive statistics

Standard deviation1024.445849
Coefficient of variation (CV)0.7622689428
Kurtosis27.90786588
Mean1343.942789
Median Absolute Deviation (MAD)291
Skewness4.331902981
Sum1127568
Variance1049489.297
MonotonicityNot monotonic
2022-01-11T16:28:27.486212image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
2.3%
14395
 
0.6%
12265
 
0.6%
12704
 
0.5%
10264
 
0.5%
13694
 
0.5%
14243
 
0.4%
10483
 
0.4%
9853
 
0.4%
12863
 
0.4%
Other values (632)786
93.7%
ValueCountFrequency (%)
019
2.3%
101
 
0.1%
162
 
0.2%
741
 
0.1%
771
 
0.1%
1151
 
0.1%
1181
 
0.1%
1331
 
0.1%
1361
 
0.1%
1371
 
0.1%
ValueCountFrequency (%)
110201
0.1%
103291
0.1%
88011
0.1%
82231
0.1%
78151
0.1%
67011
0.1%
65221
0.1%
63251
0.1%
59191
0.1%
58941
0.1%
Distinct834
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Minimum2020-01-01 10:00:01+00:00
Maximum2020-12-31 10:01:02+00:00
2022-01-11T16:28:27.748293image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:27.976389image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

n_comments
Real number (ℝ≥0)

HIGH CORRELATION

Distinct408
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.0441001
Minimum1
Maximum5702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:28.215406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q120
median92
Q3310.5
95-th percentile1333.7
Maximum5702
Range5701
Interquartile range (IQR)290.5

Descriptive statistics

Standard deviation557.7115825
Coefficient of variation (CV)1.804634297
Kurtosis19.57107595
Mean309.0441001
Median Absolute Deviation (MAD)85
Skewness3.744530566
Sum259288
Variance311042.2093
MonotonicityNot monotonic
2022-01-11T16:28:28.416421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219
 
2.3%
818
 
2.1%
314
 
1.7%
1214
 
1.7%
614
 
1.7%
113
 
1.5%
413
 
1.5%
711
 
1.3%
1011
 
1.3%
1510
 
1.2%
Other values (398)702
83.7%
ValueCountFrequency (%)
113
1.5%
219
2.3%
314
1.7%
413
1.5%
58
1.0%
614
1.7%
711
1.3%
818
2.1%
910
1.2%
1011
1.3%
ValueCountFrequency (%)
57021
0.1%
37451
0.1%
37071
0.1%
35951
0.1%
34251
0.1%
31451
0.1%
31311
0.1%
25701
0.1%
25561
0.1%
25261
0.1%

uniqueID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct839
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27
 
1
nyt://article/9872c5b0-6015-5c99-905f-7de9e3d5b107
 
1
nyt://article/e109ae37-4553-513c-b51e-5409f725716f
 
1
nyt://article/7c07b95a-1dd9-5b0d-b320-c84e1f2cc979
 
1
nyt://article/d5686346-4b09-56b4-89f3-fe1c9c291f13
 
1
Other values (834)
834 

Length

Max length54
Median length50
Mean length50.09058403
Min length50

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839 ?
Unique (%)100.0%

Sample

1st rownyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27
2nd rownyt://article/40e910f7-8d5f-5beb-9e7c-7ed27ac1597c
3rd rownyt://article/d96041df-e929-51ad-8dd1-88bb84964888
4th rownyt://article/4154be08-a296-5881-8342-e9f223dc484c
5th rownyt://article/afed02eb-f035-542a-8791-c243893e65d7

Common Values

ValueCountFrequency (%)
nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba271
 
0.1%
nyt://article/9872c5b0-6015-5c99-905f-7de9e3d5b1071
 
0.1%
nyt://article/e109ae37-4553-513c-b51e-5409f725716f1
 
0.1%
nyt://article/7c07b95a-1dd9-5b0d-b320-c84e1f2cc9791
 
0.1%
nyt://article/d5686346-4b09-56b4-89f3-fe1c9c291f131
 
0.1%
nyt://article/e7d0466b-8190-5e04-a0f8-a94d603bcf5d1
 
0.1%
nyt://article/f9777004-2651-556d-86de-0e409359f58f1
 
0.1%
nyt://article/a7c443c8-1e8f-5426-b6d6-54347c448ece1
 
0.1%
nyt://article/57e0985e-90c8-5007-8cd0-527d7d64a5f81
 
0.1%
nyt://article/39ff4653-14ed-5b5f-b599-317f9fb44c1a1
 
0.1%
Other values (829)829
98.8%

Length

2022-01-11T16:28:28.629440image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba271
 
0.1%
nyt://article/8ec51655-7bc0-552c-bdc0-f620313e7e631
 
0.1%
nyt://article/42043d2b-a73c-5c31-ba4e-457392e735c21
 
0.1%
nyt://article/d96041df-e929-51ad-8dd1-88bb849648881
 
0.1%
nyt://article/4154be08-a296-5881-8342-e9f223dc484c1
 
0.1%
nyt://article/afed02eb-f035-542a-8791-c243893e65d71
 
0.1%
nyt://article/80b0675a-7867-57f2-919e-8642e939f7471
 
0.1%
nyt://article/0e73f3b9-b692-572f-85cd-6ab8831dfffc1
 
0.1%
nyt://article/6ed0d121-21ca-54d5-b0cf-2e7cef434eb51
 
0.1%
nyt://article/ef8de76b-cb6b-55e2-9a1c-1e8165c20cb71
 
0.1%
Other values (829)829
98.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

print_section
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)2.7%
Missing252
Missing (%)30.0%
Memory size6.7 KiB
A
289 
B
82 
D
64 
C
45 
SR
 
17
Other values (11)
90 

Length

Max length2
Median length1
Mean length1.170357751
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowD
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A289
34.4%
B82
 
9.8%
D64
 
7.6%
C45
 
5.4%
SR17
 
2.0%
RE16
 
1.9%
MM15
 
1.8%
AR13
 
1.5%
MB13
 
1.5%
BR11
 
1.3%
Other values (6)22
 
2.6%
(Missing)252
30.0%

Length

2022-01-11T16:28:28.813497image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a289
49.2%
b82
 
14.0%
d64
 
10.9%
c45
 
7.7%
sr17
 
2.9%
re16
 
2.7%
mm15
 
2.6%
ar13
 
2.2%
mb13
 
2.2%
br11
 
1.9%
Other values (6)22
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

print_page
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct32
Distinct (%)5.5%
Missing252
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean8.991482112
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:28.978511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q316
95-th percentile26
Maximum36
Range35
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.603808726
Coefficient of variation (CV)0.9568843733
Kurtosis-0.4803899181
Mean8.991482112
Median Absolute Deviation (MAD)5
Skewness0.8861114949
Sum5278
Variance74.02552459
MonotonicityNot monotonic
2022-01-11T16:28:29.215529image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1158
18.8%
642
 
5.0%
239
 
4.6%
332
 
3.8%
432
 
3.8%
1019
 
2.3%
519
 
2.3%
2319
 
2.3%
1218
 
2.1%
818
 
2.1%
Other values (22)191
22.8%
(Missing)252
30.0%
ValueCountFrequency (%)
1158
18.8%
239
 
4.6%
332
 
3.8%
432
 
3.8%
519
 
2.3%
642
 
5.0%
717
 
2.0%
818
 
2.1%
915
 
1.8%
1019
 
2.3%
ValueCountFrequency (%)
361
 
0.1%
351
 
0.1%
304
 
0.5%
292
 
0.2%
282
 
0.2%
2715
1.8%
269
1.1%
254
 
0.5%
247
 
0.8%
2319
2.3%

lead_paragraph
Categorical

HIGH CARDINALITY
UNIFORM

Distinct785
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
 
15
[Want to get New York Today by email? Here’s the sign-up.]
 
12
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts | Via Spotify | Via Stitcher
 
9
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.
 
5
Click on the slide show to see this week’s featured properties:
 
4
Other values (780)
794 

Length

Max length1162
Median length232
Mean length239.2443385
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique770 ?
Unique (%)91.8%

Sample

1st rowARLINGTON, Texas — Mookie Betts was 24 years old when he wondered if he would ever be any better. He had just finished as runner-up to Mike Trout for the American League Most Valuable Player Award in 2016, with a season full of hits and homers and steals and defensive excellence. What could he do next?
2nd rowTaking frequent baths may reduce the risk for cardiovascular disease, new research suggests.
3rd rowYoung people today have become much more open-minded about gender roles — it shows up in their attitudes about pronouns, politics and sports. But in one area, change has been minimal. They are holding on to traditional views about who does what at home.
4th row新冠病毒疫情最新消息
5th rowFor months the S&P 500 rose this year — despite a deadly pandemic, the resulting economic devastation and the rise of a Democratic Party increasingly sympathetic to democratic socialism. Then, this month, with Joe Biden doing well in the polls, stock prices finally stumbled.

Common Values

ValueCountFrequency (%)
15
 
1.8%
[Want to get New York Today by email? Here’s the sign-up.]12
 
1.4%
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts | Via Spotify | Via Stitcher9
 
1.1%
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.5
 
0.6%
Click on the slide show to see this week’s featured properties:4
 
0.5%
Dear Diary:4
 
0.5%
This briefing has ended. Follow our latest coverage of the coronavirus pandemic.3
 
0.4%
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. We’re all stuck at home at the moment, so here are the 50 best movies on Netflix right now.3
 
0.4%
[Follow the DNC Live: Biden’s speech, schedule, start time, streaming and more.]2
 
0.2%
[Read our live updates on President Trump’s coronavirus diagnosis.]2
 
0.2%
Other values (775)780
93.0%

Length

2022-01-11T16:28:29.514551image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1849
 
5.5%
a1026
 
3.0%
of940
 
2.8%
to842
 
2.5%
and828
 
2.4%
in761
 
2.2%
on354
 
1.0%
that339
 
1.0%
317
 
0.9%
for304
 
0.9%
Other values (7763)26295
77.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

headline.main
Categorical

HIGH CARDINALITY
UNIFORM

Distinct838
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Homes for Sale in New York and Connecticut
 
2
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance
 
1
Bill Gates Is the Most Interesting Man in the World
 
1
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call
 
1
Already Had Plenty of Trump 2020?
 
1
Other values (833)
833 

Length

Max length112
Median length57
Mean length54.15852205
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique837 ?
Unique (%)99.8%

Sample

1st rowMookie Betts Leads Dodgers’ Stars With a Masterly Performance
2nd rowTaking Baths May Be Good for Your Heart
3rd rowYoung Men Embrace Gender Equality, but They Still Don’t Vacuum
4th row2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears Goal
5th rowIs the Stock Market Rooting for Trump or Biden?

Common Values

ValueCountFrequency (%)
Homes for Sale in New York and Connecticut2
 
0.2%
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance1
 
0.1%
Bill Gates Is the Most Interesting Man in the World1
 
0.1%
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call1
 
0.1%
Already Had Plenty of Trump 2020?1
 
0.1%
How the Virus Slowed the Booming Wind Energy Business1
 
0.1%
Coronavirus Cases Rise Sharply in Prisons Even as They Plateau Nationwide1
 
0.1%
Museums Are Back, but Different: A Visitor’s Guide1
 
0.1%
Jobless Numbers Are ‘Eye-Watering’ but Understate the Crisis1
 
0.1%
For Veterans Day, Some Former Military Officers Reflect on Lessons From Their Parents1
 
0.1%
Other values (828)828
98.7%

Length

2022-01-11T16:28:29.790573image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the312
 
4.1%
a209
 
2.7%
to163
 
2.1%
in145
 
1.9%
of143
 
1.9%
and117
 
1.5%
for88
 
1.2%
is84
 
1.1%
on64
 
0.8%
coronavirus56
 
0.7%
Other values (3094)6225
81.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.383790226
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:30.007589image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.426420502
Coefficient of variation (CV)0.5367376402
Kurtosis-1.230186716
Mean6.383790226
Median Absolute Deviation (MAD)3
Skewness0.07258068568
Sum5356
Variance11.74035745
MonotonicityNot monotonic
2022-01-11T16:28:30.157601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
588
10.5%
483
9.9%
980
9.5%
379
9.4%
1076
9.1%
1267
8.0%
167
8.0%
266
7.9%
863
7.5%
662
7.4%
Other values (2)108
12.9%
ValueCountFrequency (%)
167
8.0%
266
7.9%
379
9.4%
483
9.9%
588
10.5%
662
7.4%
750
6.0%
863
7.5%
980
9.5%
1076
9.1%
ValueCountFrequency (%)
1267
8.0%
1158
6.9%
1076
9.1%
980
9.5%
863
7.5%
750
6.0%
662
7.4%
588
10.5%
483
9.9%
379
9.4%

Has_Comments
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
1
839 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1839
100.0%

Length

2022-01-11T16:28:30.340612image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:30.444618image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
1839
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

comment_size
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
S
428 
M
213 
L
198 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowL
4th rowS
5th rowM

Common Values

ValueCountFrequency (%)
S428
51.0%
M213
25.4%
L198
23.6%

Length

2022-01-11T16:28:30.548629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:30.680636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
s428
51.0%
m213
25.4%
l198
23.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_POS_NOUN
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.05363528
Minimum0
Maximum46
Zeros27
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:30.822651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q312
95-th percentile19
Maximum46
Range46
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.063564097
Coefficient of variation (CV)0.6697380566
Kurtosis4.505171217
Mean9.05363528
Median Absolute Deviation (MAD)4
Skewness1.419551795
Sum7596
Variance36.76680956
MonotonicityNot monotonic
2022-01-11T16:28:31.021663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
970
 
8.3%
1161
 
7.3%
858
 
6.9%
456
 
6.7%
756
 
6.7%
656
 
6.7%
1055
 
6.6%
1254
 
6.4%
348
 
5.7%
542
 
5.0%
Other values (27)283
33.7%
ValueCountFrequency (%)
027
 
3.2%
134
4.1%
239
4.6%
348
5.7%
456
6.7%
542
5.0%
656
6.7%
756
6.7%
858
6.9%
970
8.3%
ValueCountFrequency (%)
461
0.1%
441
0.1%
401
0.1%
351
0.1%
341
0.1%
331
0.1%
312
0.2%
302
0.2%
291
0.1%
272
0.2%

TEXT_LeadParagraph_POS_PNOUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.468414779
Minimum0
Maximum24
Zeros130
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:31.194678image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile12
Maximum24
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.787133252
Coefficient of variation (CV)0.8475339554
Kurtosis2.061353635
Mean4.468414779
Median Absolute Deviation (MAD)2
Skewness1.226488208
Sum3749
Variance14.34237827
MonotonicityNot monotonic
2022-01-11T16:28:31.351686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0130
15.5%
3107
12.8%
2102
12.2%
585
10.1%
484
10.0%
674
8.8%
157
6.8%
756
6.7%
832
 
3.8%
930
 
3.6%
Other values (11)82
9.8%
ValueCountFrequency (%)
0130
15.5%
157
6.8%
2102
12.2%
3107
12.8%
484
10.0%
585
10.1%
674
8.8%
756
6.7%
832
 
3.8%
930
 
3.6%
ValueCountFrequency (%)
241
 
0.1%
211
 
0.1%
193
 
0.4%
173
 
0.4%
164
 
0.5%
158
1.0%
149
1.1%
138
1.0%
127
0.8%
1113
1.5%

TEXT_Keywords_POS_NOUN
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.991656734
Minimum0
Maximum17
Zeros116
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:31.504698image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.369994813
Coefficient of variation (CV)0.7922014535
Kurtosis1.980832925
Mean2.991656734
Median Absolute Deviation (MAD)2
Skewness1.063436947
Sum2510
Variance5.616875414
MonotonicityNot monotonic
2022-01-11T16:28:31.643711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2151
18.0%
1140
16.7%
3126
15.0%
0116
13.8%
4114
13.6%
572
8.6%
649
 
5.8%
731
 
3.7%
822
 
2.6%
98
 
1.0%
Other values (5)10
 
1.2%
ValueCountFrequency (%)
0116
13.8%
1140
16.7%
2151
18.0%
3126
15.0%
4114
13.6%
572
8.6%
649
 
5.8%
731
 
3.7%
822
 
2.6%
98
 
1.0%
ValueCountFrequency (%)
171
 
0.1%
141
 
0.1%
131
 
0.1%
111
 
0.1%
106
 
0.7%
98
 
1.0%
822
 
2.6%
731
3.7%
649
5.8%
572
8.6%

TEXT_Keywords_POS_PNOUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.78307509
Minimum0
Maximum146
Zeros37
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:31.828722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median12
Q319
95-th percentile30
Maximum146
Range146
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.68615635
Coefficient of variation (CV)0.7753100292
Kurtosis32.24330063
Mean13.78307509
Median Absolute Deviation (MAD)6
Skewness3.515649292
Sum11564
Variance114.1939375
MonotonicityNot monotonic
2022-01-11T16:28:32.024738image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
644
 
5.2%
944
 
5.2%
1243
 
5.1%
242
 
5.0%
1040
 
4.8%
037
 
4.4%
836
 
4.3%
1335
 
4.2%
735
 
4.2%
532
 
3.8%
Other values (39)451
53.8%
ValueCountFrequency (%)
037
4.4%
14
 
0.5%
242
5.0%
316
 
1.9%
428
3.3%
532
3.8%
644
5.2%
735
4.2%
836
4.3%
944
5.2%
ValueCountFrequency (%)
1461
0.1%
831
0.1%
811
0.1%
731
0.1%
571
0.1%
531
0.1%
511
0.1%
451
0.1%
441
0.1%
412
0.2%

TEXT_headline.main_POS_NOUN
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
0
354 
1
327 
2
121 
3
 
31
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0354
42.2%
1327
39.0%
2121
 
14.4%
331
 
3.7%
46
 
0.7%

Length

2022-01-11T16:28:32.221753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:32.342762image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0354
42.2%
1327
39.0%
2121
 
14.4%
331
 
3.7%
46
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_headline.main_POS_PNOUN
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.567342074
Minimum0
Maximum10
Zeros56
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:32.461767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.030185447
Coefficient of variation (CV)0.569103104
Kurtosis-0.3618087253
Mean3.567342074
Median Absolute Deviation (MAD)1
Skewness0.2616108381
Sum2993
Variance4.121652951
MonotonicityNot monotonic
2022-01-11T16:28:32.593782image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3153
18.2%
2144
17.2%
4136
16.2%
5132
15.7%
675
8.9%
174
8.8%
056
 
6.7%
743
 
5.1%
819
 
2.3%
95
 
0.6%
ValueCountFrequency (%)
056
 
6.7%
174
8.8%
2144
17.2%
3153
18.2%
4136
16.2%
5132
15.7%
675
8.9%
743
 
5.1%
819
 
2.3%
95
 
0.6%
ValueCountFrequency (%)
102
 
0.2%
95
 
0.6%
819
 
2.3%
743
 
5.1%
675
8.9%
5132
15.7%
4136
16.2%
3153
18.2%
2144
17.2%
174
8.8%

TEXT_LeadParagraph_ENT_ORG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4541120381
Minimum0
Maximum5
Zeros555
Zeros (%)66.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:32.722788image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7488920324
Coefficient of variation (CV)1.649134948
Kurtosis5.213057557
Mean0.4541120381
Median Absolute Deviation (MAD)0
Skewness2.028195752
Sum381
Variance0.5608392762
MonotonicityNot monotonic
2022-01-11T16:28:32.842799image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0555
66.2%
1212
 
25.3%
255
 
6.6%
310
 
1.2%
46
 
0.7%
51
 
0.1%
ValueCountFrequency (%)
0555
66.2%
1212
 
25.3%
255
 
6.6%
310
 
1.2%
46
 
0.7%
51
 
0.1%
ValueCountFrequency (%)
51
 
0.1%
46
 
0.7%
310
 
1.2%
255
 
6.6%
1212
 
25.3%
0555
66.2%

TEXT_Keywords_ENT_ORG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.497020262
Minimum0
Maximum14
Zeros262
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:32.970808image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.633386218
Coefficient of variation (CV)1.09109159
Kurtosis7.991717462
Mean1.497020262
Median Absolute Deviation (MAD)1
Skewness2.087866594
Sum1256
Variance2.667950538
MonotonicityNot monotonic
2022-01-11T16:28:33.105818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0262
31.2%
1240
28.6%
2161
19.2%
3106
12.6%
429
 
3.5%
522
 
2.6%
66
 
0.7%
74
 
0.5%
83
 
0.4%
93
 
0.4%
Other values (3)3
 
0.4%
ValueCountFrequency (%)
0262
31.2%
1240
28.6%
2161
19.2%
3106
12.6%
429
 
3.5%
522
 
2.6%
66
 
0.7%
74
 
0.5%
83
 
0.4%
93
 
0.4%
ValueCountFrequency (%)
141
 
0.1%
111
 
0.1%
101
 
0.1%
93
 
0.4%
83
 
0.4%
74
 
0.5%
66
 
0.7%
522
 
2.6%
429
 
3.5%
3106
12.6%

TEXT_LeadParagraph_ENT_NORP
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1811680572
Minimum0
Maximum5
Zeros720
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:33.238828image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5129148944
Coefficient of variation (CV)2.83115524
Kurtosis21.02227048
Mean0.1811680572
Median Absolute Deviation (MAD)0
Skewness3.910939011
Sum152
Variance0.2630816889
MonotonicityNot monotonic
2022-01-11T16:28:33.376837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0720
85.8%
196
 
11.4%
217
 
2.0%
33
 
0.4%
42
 
0.2%
51
 
0.1%
ValueCountFrequency (%)
0720
85.8%
196
 
11.4%
217
 
2.0%
33
 
0.4%
42
 
0.2%
51
 
0.1%
ValueCountFrequency (%)
51
 
0.1%
42
 
0.2%
33
 
0.4%
217
 
2.0%
196
 
11.4%
0720
85.8%

TEXT_Keywords_ENT_NORP
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
0
785 
1
 
52
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0785
93.6%
152
 
6.2%
22
 
0.2%

Length

2022-01-11T16:28:33.538848image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:33.645856image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0785
93.6%
152
 
6.2%
22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_ENT_FAC
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
0
806 
1
 
31
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0806
96.1%
131
 
3.7%
22
 
0.2%

Length

2022-01-11T16:28:33.744866image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:33.838872image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0806
96.1%
131
 
3.7%
22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_Keywords_ENT_FAC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
0
831 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0831
99.0%
18
 
1.0%

Length

2022-01-11T16:28:33.938877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:34.046887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0831
99.0%
18
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_ENT_GPE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7604290822
Minimum0
Maximum7
Zeros450
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:34.136895image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.07079156
Coefficient of variation (CV)1.408141252
Kurtosis5.343947323
Mean0.7604290822
Median Absolute Deviation (MAD)0
Skewness1.955458291
Sum638
Variance1.146594565
MonotonicityNot monotonic
2022-01-11T16:28:34.255902image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0450
53.6%
1235
28.0%
295
 
11.3%
338
 
4.5%
412
 
1.4%
56
 
0.7%
73
 
0.4%
ValueCountFrequency (%)
0450
53.6%
1235
28.0%
295
 
11.3%
338
 
4.5%
412
 
1.4%
56
 
0.7%
73
 
0.4%
ValueCountFrequency (%)
73
 
0.4%
56
 
0.7%
412
 
1.4%
338
 
4.5%
295
 
11.3%
1235
28.0%
0450
53.6%

TEXT_Keywords_ENT_GPE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8915375447
Minimum0
Maximum24
Zeros406
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:34.387910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.480634707
Coefficient of variation (CV)1.6607654
Kurtosis83.79575575
Mean0.8915375447
Median Absolute Deviation (MAD)1
Skewness6.762831153
Sum748
Variance2.192279137
MonotonicityNot monotonic
2022-01-11T16:28:34.536923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0406
48.4%
1257
30.6%
2115
 
13.7%
340
 
4.8%
411
 
1.3%
74
 
0.5%
52
 
0.2%
141
 
0.1%
131
 
0.1%
241
 
0.1%
ValueCountFrequency (%)
0406
48.4%
1257
30.6%
2115
 
13.7%
340
 
4.8%
411
 
1.3%
52
 
0.2%
74
 
0.5%
81
 
0.1%
131
 
0.1%
141
 
0.1%
ValueCountFrequency (%)
241
 
0.1%
141
 
0.1%
131
 
0.1%
81
 
0.1%
74
 
0.5%
52
 
0.2%
411
 
1.3%
340
 
4.8%
2115
13.7%
1257
30.6%

TEXT_LeadParagraph_ENT_LOC
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
0
792 
1
 
42
2
 
4
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0792
94.4%
142
 
5.0%
24
 
0.5%
31
 
0.1%

Length

2022-01-11T16:28:34.679934image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:34.779940image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0792
94.4%
142
 
5.0%
24
 
0.5%
31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_Keywords_ENT_LOC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
0
807 
1
 
29
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0807
96.2%
129
 
3.5%
23
 
0.4%

Length

2022-01-11T16:28:34.901948image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-11T16:28:35.003958image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0807
96.2%
129
 
3.5%
23
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_ENT_PERSON
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6507747318
Minimum0
Maximum6
Zeros461
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:35.096966image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9065375107
Coefficient of variation (CV)1.393012768
Kurtosis4.91928856
Mean0.6507747318
Median Absolute Deviation (MAD)0
Skewness1.882145386
Sum546
Variance0.8218102583
MonotonicityNot monotonic
2022-01-11T16:28:35.220973image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0461
54.9%
1263
31.3%
282
 
9.8%
320
 
2.4%
47
 
0.8%
55
 
0.6%
61
 
0.1%
ValueCountFrequency (%)
0461
54.9%
1263
31.3%
282
 
9.8%
320
 
2.4%
47
 
0.8%
55
 
0.6%
61
 
0.1%
ValueCountFrequency (%)
61
 
0.1%
55
 
0.6%
47
 
0.8%
320
 
2.4%
282
 
9.8%
1263
31.3%
0461
54.9%

TEXT_Keyrwords_ENT_PERSON
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.887961859
Minimum0
Maximum18
Zeros262
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-01-11T16:28:35.358985image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.256644502
Coefficient of variation (CV)1.195280768
Kurtosis8.863595042
Mean1.887961859
Median Absolute Deviation (MAD)1
Skewness2.305513681
Sum1584
Variance5.092444409
MonotonicityNot monotonic
2022-01-11T16:28:35.487994image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0262
31.2%
1208
24.8%
2139
16.6%
374
 
8.8%
467
 
8.0%
534
 
4.1%
624
 
2.9%
79
 
1.1%
106
 
0.7%
85
 
0.6%
Other values (4)11
 
1.3%
ValueCountFrequency (%)
0262
31.2%
1208
24.8%
2139
16.6%
374
 
8.8%
467
 
8.0%
534
 
4.1%
624
 
2.9%
79
 
1.1%
85
 
0.6%
94
 
0.5%
ValueCountFrequency (%)
182
 
0.2%
151
 
0.1%
114
 
0.5%
106
 
0.7%
94
 
0.5%
85
 
0.6%
79
 
1.1%
624
 
2.9%
534
4.1%
467
8.0%

Interactions

2022-01-11T16:28:17.397031image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:03.094786image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:09.215442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:15.058875image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:21.208987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:28.551382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:34.377441image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:39.891475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:44.428475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:48.048167image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:52.850520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:56.226769image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:59.595023image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:02.951272image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:07.012947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:10.538523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:13.803763image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:17.629046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:03.572823image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:09.589470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:15.379896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:21.519914image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:28.930245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:34.676590image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:40.269504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:44.660489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:48.267182image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:53.153542image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:56.411784image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:59.780034image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:03.148286image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:07.251966image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:10.742539image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:14.029778image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:17.855067image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:03.829844image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:09.807484image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:15.762923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:21.831855image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:29.299714image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:34.854601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:40.694535image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:44.861504image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:48.468198image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:53.336556image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:56.620800image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:59.962046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:03.352302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:07.461981image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:10.941557image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:14.202796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:18.061079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:04.064864image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:10.029503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:16.061945image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:22.298931image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:29.682806image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:35.109121image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:41.034560image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:45.077016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:48.681211image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:53.516569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:56.829818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:00.162063image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:03.932342image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:07.646990image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:11.127563image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:14.363803image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:18.416109image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:04.615898image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:10.366522image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:16.562990image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:22.780967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:30.253859image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:35.436146image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:41.360582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:45.378039image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:48.960234image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:53.834597image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:57.159839image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:00.425083image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:04.247275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:07.946013image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:11.399586image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:14.615822image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:18.693128image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:04.972926image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:10.618545image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:16.940014image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:23.083600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:30.533875image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:35.689165image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:41.567626image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:45.584974image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:49.152249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:54.002604image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:57.366855image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:00.651099image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:04.469293image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:08.149029image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:11.578598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:14.819837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:18.931145image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:05.332952image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:10.983573image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:17.281037image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:23.490178image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:30.835898image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:35.985185image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:41.771631image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:45.791986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:49.347260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:54.179617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:57.558869image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:00.847116image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:04.667315image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:08.336042image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:11.784617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:15.044857image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:19.148160image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:05.708982image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:11.328598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:17.641068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:24.323653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:31.143922image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:36.237206image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:41.977650image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:45.986000image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:49.532274image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:54.343631image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:57.755885image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:01.035132image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:04.855322image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:08.543061image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:11.959632image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:15.229867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:19.381178image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:06.114019image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:11.711623image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:17.897087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:24.757749image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:31.451256image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:36.537226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:42.236835image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:46.210031image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:49.746294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:54.535647image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:57.969902image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:01.227143image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:05.086341image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:08.773079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:12.158644image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:15.432884image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:19.634195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:06.526042image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:12.081652image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:18.266112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:25.052932image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:31.759917image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:36.816247image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:42.447853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:46.440048image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:50.387339image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:54.717661image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:58.165917image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:01.414154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:05.278353image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:08.977090image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:12.345654image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:15.630901image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:20.434254image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:06.891073image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:12.362672image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:18.604137image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:25.363227image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:32.112956image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:37.494301image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:42.711904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:46.629059image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:50.625355image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:54.892675image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:58.347926image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:01.598171image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:05.495368image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:09.180011image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:12.531669image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:15.831915image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:20.647270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:07.226094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:12.935717image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:18.949159image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:25.743656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:32.442630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:37.699313image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:43.011926image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:46.819071image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:50.858377image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:55.064687image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:58.511941image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:01.771182image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:05.684384image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:09.369026image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:12.720683image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:16.058931image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:20.890289image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:07.627127image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:13.272741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:19.329187image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:26.114689image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:32.800705image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:37.919333image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:43.333947image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:47.027087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:51.101390image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:55.253700image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:58.686955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:01.961197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:05.924402image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:09.578054image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:12.899696image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:16.363955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:21.111304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:07.963591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:13.607767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:19.732221image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:26.608315image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:33.169843image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:38.199356image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:43.547962image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:47.224103image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:51.410415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:55.433715image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:58.851964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:02.164215image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:06.111412image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:09.774467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:13.073710image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:16.566969image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:21.315320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:08.257367image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:14.002795image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:20.097070image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:26.928339image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:33.511043image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:38.557381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:43.815423image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:47.433120image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:51.778446image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:55.634732image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:59.034979image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:02.354226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:06.332430image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:09.981478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:13.269724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:16.783988image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:21.515339image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:08.494383image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:14.399824image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:20.464275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:27.428481image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:33.729631image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:38.932406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:44.030435image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:47.645133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:52.109467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:55.820740image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:59.216993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:02.547240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:06.581915image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:10.164494image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:13.436735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:16.978999image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:21.718354image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:08.809412image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:14.669844image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:20.832963image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:27.999156image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:33.968650image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:39.392440image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:44.224461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:47.846153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:52.447493image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:56.027756image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:27:59.405008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:02.737255image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:06.793929image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:10.334507image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:13.615749image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-01-11T16:28:17.195015image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-01-11T16:28:36.327057image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-11T16:28:36.812092image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-11T16:28:37.310131image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-11T16:28:37.758163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-11T16:28:38.072187image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-11T16:28:22.125382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-11T16:28:23.936517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-11T16:28:24.513105image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-11T16:28:24.715122image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexnewsdesksectionmaterialheadlineabstractkeywordsword_countpub_daten_commentsuniqueIDprint_sectionprint_pagelead_paragraphheadline.mainmonthHas_Commentscomment_sizeTEXT_LeadParagraph_POS_NOUNTEXT_LeadParagraph_POS_PNOUNTEXT_Keywords_POS_NOUNTEXT_Keywords_POS_PNOUNTEXT_headline.main_POS_NOUNTEXT_headline.main_POS_PNOUNTEXT_LeadParagraph_ENT_ORGTEXT_Keywords_ENT_ORGTEXT_LeadParagraph_ENT_NORPTEXT_Keywords_ENT_NORPTEXT_LeadParagraph_ENT_FACTEXT_Keywords_ENT_FACTEXT_LeadParagraph_ENT_GPETEXT_Keywords_ENT_GPETEXT_LeadParagraph_ENT_LOCTEXT_Keywords_ENT_LOCTEXT_LeadParagraph_ENT_PERSONTEXT_Keyrwords_ENT_PERSON
013715SportsSportsNewsMookie Betts Leads Dodgers’ Stars With a Masterly PerformanceThe Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.['Baseball', 'World Series', 'Boston Red Sox', 'Los Angeles Dodgers', 'Tampa Bay Rays', 'Bellinger, Cody (1995- )', 'Betts, Mookie (1992- )', 'Kershaw, Clayton']10832020-10-21 13:23:08+00:0018nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27B10ARLINGTON, Texas — Mookie Betts was 24 years old when he wondered if he would ever be any better. He had just finished as runner-up to Mike Trout for the American League Most Valuable Player Award in 2016, with a season full of hits and homers and steals and defensive excellence. What could he do next?Mookie Betts Leads Dodgers’ Stars With a Masterly Performance101S81231724130000200023
14455WellWellNewsTaking Baths May Be Good for Your HeartDaily baths reduced the risk of heart disease and stroke.['Bathing and Showering', 'Heart', 'Blood Pressure']2422020-04-01 16:34:18+00:0014nyt://article/40e910f7-8d5f-5beb-9e7c-7ed27ac1597cD6Taking frequent baths may reduce the risk for cardiovascular disease, new research suggests.Taking Baths May Be Good for Your Heart41S403220000000000000
21886UpshotThe UpshotNewsYoung Men Embrace Gender Equality, but They Still Don’t VacuumNew studies show traditional views persist about who does what at home, and it’s holding women back.['Work-Life Balance', 'Women and Girls', 'Research', 'Men and Boys', 'Parenting', 'Labor and Jobs', 'United States']13152020-02-11 10:00:12+00:00632nyt://article/d96041df-e929-51ad-8dd1-88bb84964888B5Young people today have become much more open-minded about gender roles — it shows up in their attitudes about pronouns, politics and sports. But in one area, change has been minimal. They are holding on to traditional views about who does what at home.Young Men Embrace Gender Equality, but They Still Don’t Vacuum21L1208504000000010000
37305ForeignWorldNews2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears GoalThe Chinese city where the outbreak began is seeking to test all its 11 million residents, and the pandemic has forced the fashion industry to take a hard look in the mirror.['Coronavirus (2019-nCoV)']38052020-05-26 04:05:17+00:0061nyt://article/4154be08-a296-5881-8342-e9f223dc484cNaNNaN新冠病毒疫情最新消息2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears Goal51S010224000000000000
412331OpEdOpinionOp-EdIs the Stock Market Rooting for Trump or Biden?Neither. Wall Street is not as partisan as you think.['Presidential Election of 2020', 'United States Economy', 'Stocks and Bonds']8802020-09-21 09:00:11+00:00317nyt://article/afed02eb-f035-542a-8791-c243893e65d7A23For months the S&P 500 rose this year — despite a deadly pandemic, the resulting economic devastation and the rise of a Democratic Party increasingly sympathetic to democratic socialism. Then, this month, with Joe Biden doing well in the polls, stock prices finally stumbled.Is the Stock Market Rooting for Trump or Biden?91M1053505110000000010
56152PoliticsU.S.NewsWhy Biden’s Choice of Running Mate Has Momentous ImplicationsJoe Biden has hinted that he might serve only one term if he wins. That would set up a woman as the front-runner for 2024 and perhaps define the Democratic agenda for the next decade.['Presidential Election of 2020', 'Vice Presidents and Vice Presidency (US)', 'United States Politics and Government', 'Presidential Election of 2024', 'Biden, Joseph R Jr', 'Women and Girls', 'Democratic Party', 'Abrams, Stacey Y', 'Demings, Val', 'Grisham, Michelle Lujan', 'Harris, Kamala D', 'Klobuchar, Amy', 'Warren, Elizabeth', 'Rice, Susan E']17252020-05-03 17:48:35+00:00290nyt://article/80b0675a-7867-57f2-919e-8642e939f747A1WASHINGTON — For decades the vice-presidential selection process has had an air of cloak-and-dagger to it. The party’s nominees would say little about their thinking, the would-be running mates would reveal even less, and an elaborate game of subterfuge would unfold that mostly captivated political insiders and usually had little bearing on the election.Why Biden’s Choice of Running Mate Has Momentous Implications51M161238140200001100010
614233LearningThe Learning NetworkNewsLesson of the Day: ‘Lil Buck Feels the Dancing Spirit All Over Again’In this lesson, students watch a short film by the dancer Lil Buck, then consider his contention that street dance is fine art and can be “a tool to help bring change.”[]7882020-11-02 10:00:03+00:006nyt://article/0e73f3b9-b692-572f-85cd-6ab8831dfffcNaNNaNFeatured Article: “Lil Buck Feels the Dancing Spirit All Over Again,” by Gia KourlasLesson of the Day: ‘Lil Buck Feels the Dancing Spirit All Over Again’111S160006000010000010
715496StylesFashion & StyleNewsAlicia Keys Figures Out Her SkinThe singer-songwriter talks about finding her rituals (and learning how to deal with carbs).['Keys, Alicia', 'Keys Soulcare (ELF Cosmetics Inc)', 'Cosmetics and Toiletries', 'Skin', 'Exercise', 'Meditation', 'Quarantine (Life and Culture)', 'Content Type: Personal Profile', 'Rhythm and Blues (Music)']12262020-12-01 17:13:41+00:0016nyt://article/6ed0d121-21ca-54d5-b0cf-2e7cef434eb5D3Alicia Keys Figures Out Her Skin121S0061612010000000003
810579WeekendBooksNewsShe Explains ‘Mansplaining’ With Help From 17th-Century ArtIn her new book “Men to Avoid in Art and Life,” Nicole Tersigni harnesses her skill with a Twitter meme to illuminate the experience of women harassed by concern trolls, “sexperts” and more.['Books and Literature', 'Women and Girls', 'Comedy and Humor', 'Social Media', 'Writing and Writers', 'Art', 'Discrimination', 'Tersigni, Nicole', 'Men to Avoid in Art and Life (Book)']8932020-08-10 09:00:25+00:001523nyt://article/ef8de76b-cb6b-55e2-9a1c-1e8165c20cb7C12This story begins, as so many do these days, on Twitter.She Explains ‘Mansplaining’ With Help From 17th-Century Art81L2181031020000000002
96338PodcastsPodcastsNewsA Socially Distanced SenateThe two chambers of Congress received the same medical advice about reconvening. They took different decisions.['United States Politics and Government', 'Coronavirus (2019-nCoV)', 'House of Representatives', 'Senate', 'Quarantines', 'Washington (DC)', 'Capitol Building (Washington, DC)']2842020-05-06 09:59:07+00:006nyt://article/f229cb93-f6ab-5bef-bac4-89f3449d1721NaNNaNListen and subscribe to our podcast from your mobile device:Via Apple Podcasts | Via Spotify | Via StitcherA Socially Distanced Senate51S4611502030000020001

Last rows

df_indexnewsdesksectionmaterialheadlineabstractkeywordsword_countpub_daten_commentsuniqueIDprint_sectionprint_pagelead_paragraphheadline.mainmonthHas_Commentscomment_sizeTEXT_LeadParagraph_POS_NOUNTEXT_LeadParagraph_POS_PNOUNTEXT_Keywords_POS_NOUNTEXT_Keywords_POS_PNOUNTEXT_headline.main_POS_NOUNTEXT_headline.main_POS_PNOUNTEXT_LeadParagraph_ENT_ORGTEXT_Keywords_ENT_ORGTEXT_LeadParagraph_ENT_NORPTEXT_Keywords_ENT_NORPTEXT_LeadParagraph_ENT_FACTEXT_Keywords_ENT_FACTEXT_LeadParagraph_ENT_GPETEXT_Keywords_ENT_GPETEXT_LeadParagraph_ENT_LOCTEXT_Keywords_ENT_LOCTEXT_LeadParagraph_ENT_PERSONTEXT_Keyrwords_ENT_PERSON
82916119TravelTravelNewsHow to Pretend You’re in Singapore TonightYou can feel like you are in the Lion City with a little work in the kitchen, the right book and some time in front of the TV.['Cooking and Cookbooks', 'Movies', 'Travel and Vacations', 'Food', 'Television', 'Restaurants', 'Books and Literature', 'Bourdain, Anthony', 'Kwan, Kevin', 'Liew, Sonny', 'Singapore']14322020-12-15 10:00:25+00:0064nyt://article/a511396b-640b-5be3-92e6-b0497bf6611eNaNNaNIt took over a dozen visits to Singapore for me to fall in love with it. But when I did, I fell hard. As a teenager living in Jakarta, Indonesia — just under two hours away by direct flight — I looked at Singapore’s shiny veneer and dismissed the whole place as shallow and materialistic. It was one big shopping mall, I thought, with too many rules and not enough character. But then, as I kept going back, I intentionally squashed my preconceptions and I started noticing other things. I quickly realized how much I had been missing.How to Pretend You’re in Singapore Tonight121S14451102020000310002
83012843WashingtonU.S.NewsTrump Tests Positive for the CoronavirusThe president’s result came after he spent months playing down the severity of the outbreak that has killed more than 207,000 in the United States and hours after insisting that “the end of the pandemic is in sight.”['Trump, Donald J', 'Coronavirus (2019-nCoV)', 'United States Politics and Government', 'Presidents and Presidency (US)', 'Twenty-Fifth Amendment (US Constitution)', 'Presidential Election of 2020', 'White House Coronavirus Outbreak (2020)']19142020-10-02 05:01:16+00:003707nyt://article/de217dd9-3383-574a-9eaf-c0303570e794NaNNaN[Read our live updates on President Trump’s coronavirus diagnosis.]Trump Tests Positive for the Coronavirus101L2312203010000010012
8316238MetroNew YorkNews2 Die From the Virus at a Bronx Bus Depot, and Drivers Are RattledOn the front line of the city’s battle against the pandemic, bus drivers are struggling to come to terms with their role as essential workers.['Buses', 'Coronavirus (2019-nCoV)', 'Transit Systems', 'Bronx (NYC)', 'Workplace Hazards and Violations', 'Metropolitan Transportation Authority', 'New York City', 'New York City Transit Authority']16862020-05-05 07:00:11+00:00187nyt://article/769ee82a-293d-5203-bc8c-e37cae61c6c3A1Angel Volquez was already on edge. For weeks, the New York City bus driver had watched as the city’s grim new reality played out in front of him through the large glass windshield like a dystopian movie.2 Die From the Virus at a Bronx Bus Depot, and Drivers Are Rattled51M10512014030000130012
8326615LearningThe Learning NetworkNewsShould Students Be Monitored When Taking Online Tests?Is surveillance necessary to prevent students from cheating during online exams, or does it violate students’ privacy?[]7642020-05-12 09:00:05+00:00301nyt://article/20c9eb36-46a9-565f-ba13-22e737a72eb9NaNNaNHas cheating on tests ever been a problem at your school? What about now that school has gone online? What steps do you think teachers and professors should take to ensure that students are completing online exams honestly? Is there a point when online surveillance impedes on students’ privacy — or even becomes “creepy”?Should Students Be Monitored When Taking Online Tests?51M1410012000000000000
83314143OpEdOpinionOp-EdThe Woman President Who Wasn’tTrump may have beaten Hillary Clinton, but the story doesn’t end there.['Presidential Election of 2020', 'Women and Girls', 'United States Politics and Government', 'Elections, Senate', "Women's Rights", 'Biden, Joseph R Jr', 'Senate', 'Trump, Donald J', 'Clinton, Hillary Rodham']9112020-10-30 09:00:14+00:00171nyt://article/d90c1075-cad0-55e1-bd3b-015f40de473eSR11One of my clearest memories of election night in 2016 is running into women who were going to watch the results with their daughters, so they’d get to share the experience of seeing Hillary Clinton elected president — the moment when the political glass ceiling in America would be shattered forever.The Woman President Who Wasn’t101M11342002020000100016
8341518ForeignWorldNewsBeijing in the Time of Coronavirus: No Traffic, Empty Parks and FearThe Chinese capital, like other cities far from the epidemic’s center, has imposed restrictions and shut down public spaces, straining the ties that bind society.['Coronavirus (2019-nCoV)', 'Beijing (China)', 'Shopping and Retail', 'Politics and Government', 'Epidemics', 'Parks and Other Recreation Areas', 'Communist Party of China', 'China']12732020-02-03 16:06:58+00:0018nyt://article/40edd806-d950-5b6f-a751-d7ddbc3e9f92A7BEIJING — The Apple stores were among the busiest places still open in Beijing after the coronavirus outbreak, though employees forbade customers to try the watches or AirPods.Beijing in the Time of Coronavirus: No Traffic, Empty Parks and Fear21S7441315110000220001
8359970BooksBooksNewsThe Celebrity Bookshelf Detective Is BackWe peer over the shoulders of Gwyneth Paltrow, Regina King, Charlamagne tha God, Yo-Yo Ma and others for a glimpse at their reading habits.['Celebrities', 'Quarantine (Life and Culture)', 'Books and Literature', 'Videophones and Videoconferencing', 'Hanks, Tom', 'LuPone, Patti', 'Penn, Sean', 'Paltrow, Gwyneth', 'Charlamagne Tha God', 'Powell, Colin L', 'King, Regina (1971- )', 'Ma, Yo-Yo']10942020-07-27 09:00:25+00:00156nyt://article/a4c60180-9a43-5fca-8109-8d5c3bc72787BR27Zoom happy hours are far less exciting now than they were in March. So is sourdough starter. A lot of the early preoccupations of our lockdown life don’t quite have the same charm anymore. (Remember the night we all made lasagna together?) But the chance to speculate about the minds and proclivities of the famous by gawking at their bookshelves never gets old. So after a first foray into bookshelf sleuthing, we’re back for more.The Celebrity Bookshelf Detective Is Back71M15232503030000010004
8365899ForeignWorldNewsGenoa’s New Bridge Nears Completion, Turning Tragedy Into HopeNearly two years after 43 people died when a bridge collapsed, its replacement, built in record time, has become a symbol of Italian can-do.['Morandi Bridge (Genoa, Italy)', 'Bridges and Tunnels', 'Infrastructure (Public Works)', 'Piano, Renzo', 'Roads and Traffic', 'Conte, Giuseppe', 'Salini Impregilo SpA', 'Fincantieri', 'Genoa (Italy)']11642020-04-28 14:23:28+00:0056nyt://article/fd07ece0-584c-5199-bad0-177ff5cc972eA18ROME — When the Morandi Bridge, a vital east-west transportation artery in the heart of Genoa, collapsed on Aug. 14, 2018, killing 43 people, there was little reason to think that its replacement would be in the final phases of construction less than two years later.Genoa’s New Bridge Nears Completion, Turning Tragedy Into Hope41S10641706030000020025
8373826The UpshotThe UpshotInteractive FeatureCoronavirus Deaths by U.S. State and Country Over Time: Daily TrackerCompare the number of deaths and the rate of increase over time in the places the virus has hit hardest so far.['Coronavirus (2019-nCoV)', 'States (US)', 'Epidemics', 'Deaths (Fatalities)', 'United States', 'Spain', 'France', 'Italy', 'China', 'South Korea', 'Germany']02020-03-21 14:21:03+00:001761nyt://interactive/ac617f90-cbd1-5f17-abe4-db5bb065dce3NaNNaNCompare the number of deaths and the rate of increase over time in the places the virus has hit hardest so far.Coronavirus Deaths by U.S. State and Country Over Time: Daily Tracker31L7031308000000080000
8381564NationalU.S.NewsPets Are Just ‘Property,’ So Owners Can’t Do Much When Vets Harm ThemDoctors who harm their patients face costly lawsuits and other serious consequences. There is much less accountability for veterinarians, as devastated pet owners in Oregon learned.['Veterinary Medicine', 'Animal Abuse, Rights and Welfare', 'Pets', 'Dogs', 'Cats', 'California', 'Oregon', 'Koller, Daniel', 'Regulation and Deregulation of Industry', 'Suits and Litigation (Civil)']18462020-02-04 10:00:27+00:00293nyt://article/a55a55bd-8221-508e-a704-f5da1a6c9b3bA12BEAVERTON, Ore. — After his dog Bleu sustained a leg injury over the summer, Andres Figueroa brought the 7-month-old dachshund in for a checkup at a sleek suburban clinic outside Portland, Ore., that was decorated with cutouts of cheerful pets.Pets Are Just ‘Property,’ So Owners Can’t Do Much When Vets Harm Them21M10761331010000320022